SPPI: Software Process and Product Improvement

Motivation

The size, complexity, and criticality of software systems – today often being systems of systems with both AI and software components – require innovative and economic approaches to development and evolution. In today’s competitive world, software quality is a key to success and stability of organizations. Software process and product improvement (SPPI) aims at significantly increasing both the quality of software systems and the productivity of software development. The SPPI track will bring together researchers and practitioners to share SPPI innovations and experiences. The track is an integral part of the 52nd Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2026.

Topics

Topics of interest include, but are not restricted to:

  • Organizational and business views on process improvement
  • Value-based software engineering
  • Global software engineering
  • Quality assurance/monitoring, inspections, testing
  • Software process improvement and process standards
  • Process modeling, composition, and enactment/simulation
  • Quantitative models and big data for development processes and products
  • Essential system quality aspects, e.g., dependability, safety, security, or usability
  • Technical debt
  • Open source software and software quality
  • Agile and lean development
  • Software reuse, variability management, product lines, and software ecosystems
  • Software evolution
  • Continuous delivery/integration and DevOps, software process and product evolution with feedback from operation.
  • Empirical studies and experimental approaches
  • Process improvement for innovative/emerging application areas, e.g.,
    • machine learning
    • (generative) AI and large language models
    • systems of systems
    • cloud/fog-based computing
    • big data systems
    • cyber-physical systems, IoT, and Industry 4.0.

In particular, we encourage submissions demonstrating the benefits or limitations of SPPI approaches through case studies, experiments, and quantitative data.

Track Organizers

  • Stefan Biffl, TU Wien, Austria, http://qse.ifs.tuwien.ac.at/~biffl
  • Rick Rabiser, JKU Linz, Austria, https://rickrabiser.github.io/rick/
  • Dietmar Winkler, Center for Digital Production (CDP) and TU Wien, Austria, http://qse.ifs.tuwien.ac.at/~winkler

Program Committee

  • Rami Almwari, Brunel University London
  • Gustavo Carvalho, Universidade Federal de Pernambuco
  • Steve Counsell, Brunel University
  • Matteo Esposito, University of Oulu
  • Rachel Harrison, University of Oxford Brookes
  • Sara Hassan, Birmingham City University
  • Marco Kuhrmann, Reutlingen University
  • Sherlock Licorish, University of Otago
  • Alistair Mcewan, University of Derby
  • Manuela Petrescu, Babes-Bolyai University
  • Gleison Santos, UNIRIO
  • Érica Souza, UTFPR